Augmented Analytics Techniques for Generating Data Visualizations and Actionable Insights

2. The system of claim 1, wherein the identification of the potential features of interest from the identified features includes, for each of the identified features, calculating the respective identified feature's variance.

3. The system of claim 2, wherein the identification of the potential features of interest from the identified features further includes normalizing the variances and designating a predetermined number of the identified features having the highest normalized variances as the potential features of interest.

4. The system of claim 2, wherein the identification of the potential features of interest from the identified features further includes designating a predetermined number of the identified features having the highest variances as the potential features of interest.

5. The system of claim 4, wherein the predetermined number is user-configurable.

7. The system of claim 6, wherein the chart type for each related feature is determined automatically and is changeable in response to user input.

8. The system of claim 1, wherein the generation of the display including the visual representation for each related feature includes creating a bar chart for each related feature, and wherein the representations of data values are bars in the bar charts.

9. The system of claim 1, wherein the algorithms are run so as to identify, as a related feature, each related feature for which a corresponding visual representation was selected in a previous repetition.

10. The system of claim 1, wherein the display is generated to include a visual representation for each related feature for which a corresponding visual representation was selected in a previous repetition.

11. The system of claim 1, wherein the algorithms are run on a common set of the identified features across each repetition, regardless of whether any identified features have been identified as related features, and wherein different algorithms are runnable across successive repetitions.

13. The system of claim 12, wherein the determination of which one of the plurality of classes of algorithms is to be run is based on the data type of the selected feature of interest, wherein the class of algorithms to be run is variable across successive repetitions.

15. The system of claim 1, wherein a plurality of related features is identified.

16. The system of claim 1, wherein the processing resources are further configured to respond to the number of features returned in (a) falling below a threshold by performing (b) and preventing (c) and (d).

17. The system of claim 1, wherein the processing resources are further configured to respond to the number of features returned in (a) falling below a threshold by performing (b) and preventing (c) and (d) in response to a majority of the features returned in (a) having a relevance-related score less than a predetermined value.

22. The method of claim 19, wherein the algorithms are run on a common set of the identified features across each repetition, regardless of whether any identified features have been identified as related features.

25. The method of claim 19, further comprising, in response to the number of features returned in (a) falling below a threshold, (i) generating a user prompt, and (ii) performing (b) and preventing (c) and (d).

26. The method of claim 25, wherein (i) and (ii) are performed in response to a majority of the features returned in (a) having a relevance-related score less than a predetermined value.